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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/jetpack.html">Torch-TensorRT in JetPack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/quick_start.html">Quick Start</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/capture_and_replay.html">Introduction</a></li>
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<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
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<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
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<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/compile_hf_models.html">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/notebooks.html">Legacy notebooks</a></li>
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<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../py_api/fx.html">torch_tensorrt.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../py_api/ts.html">torch_tensorrt.ts</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../cli/torchtrtc.html">torchtrtc</a></li>
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<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../contributors/system_overview.html">System Overview</a></li>
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  <h1>Source code for torch_tensorrt.dynamo._exporter</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">base64</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">copy</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">operator</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">cast</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch._guards</span><span class="w"> </span><span class="kn">import</span> <span class="n">detect_fake_mode</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch._subclasses.fake_tensor</span><span class="w"> </span><span class="kn">import</span> <span class="n">FakeTensor</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.export</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExportedProgram</span><span class="p">,</span> <span class="n">ExportGraphSignature</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.export.exported_program</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">CustomObjArgument</span><span class="p">,</span>
    <span class="n">InputKind</span><span class="p">,</span>
    <span class="n">InputSpec</span><span class="p">,</span>
    <span class="n">ModuleCallEntry</span><span class="p">,</span>
    <span class="n">ModuleCallSignature</span><span class="p">,</span>
    <span class="n">OutputKind</span><span class="p">,</span>
    <span class="n">OutputSpec</span><span class="p">,</span>
    <span class="n">TensorArgument</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.runtime._TorchTensorRTModule</span><span class="w"> </span><span class="kn">import</span> <span class="n">ENGINE_IDX</span><span class="p">,</span> <span class="n">NAME_IDX</span>


<div class="viewcode-block" id="export"><a class="viewcode-back" href="../../../py_api/dynamo.html#torch_tensorrt.dynamo.export">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">export</span><span class="p">(</span>
    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
    <span class="n">cross_compile_module</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ExportedProgram</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Export the result of TensorRT compilation into the desired output format.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        gm (torch.fx.GraphModule): Compiled Torch-TensorRT module, generated by ``torch_tensorrt.dynamo.compile``</span>
<span class="sd">        inputs (torch.Tensor): Torch input tensors</span>
<span class="sd">        cross_compile_module (bool): Flag to indicated whether it is cross_compilation enabled or not</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">patched_module</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">cross_compile_module</span><span class="p">)</span>
    <span class="n">exp_program</span> <span class="o">=</span> <span class="n">create_trt_exp_program</span><span class="p">(</span><span class="n">patched_module</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">exp_program</span></div>


<span class="k">def</span><span class="w"> </span><span class="nf">transform</span><span class="p">(</span>
    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
    <span class="n">cross_compile_module</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Transforms the graphmodule by inlining Pytorch and TensorRT submodules.</span>
<span class="sd">    Inlining collapses submodules into nodes which is necessary for torch.export</span>
<span class="sd">    serialization.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        gm (torch.fx.GraphModule): Compiled Torch-TensorRT module, generated by ``torch_tensorrt.dynamo.compile``</span>
<span class="sd">        inputs (torch.Tensor): Torch input tensors</span>
<span class="sd">        cross_compile_module (bool): Flag to indicated whether it is cross_compilation enabled or not</span>

<span class="sd">    Returns an inlined torch.fx.GraphModule</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># Make a copy the graph since this function transforms the input graph and changes it&#39;s attributes.</span>
    <span class="c1"># This transformed graph is meant to be consumed by `create_trt_exp_program`</span>
    <span class="n">gm</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">gm</span><span class="p">)</span>

    <span class="c1"># Inline TensorRT submodules</span>
    <span class="n">inline_trt_modules</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">cross_compile_module</span><span class="p">)</span>

    <span class="c1"># Inline pytorch submodules</span>
    <span class="n">inline_torch_modules</span><span class="p">(</span><span class="n">gm</span><span class="p">)</span>

    <span class="c1"># Clean the graph</span>
    <span class="n">gm</span><span class="o">.</span><span class="n">delete_all_unused_submodules</span><span class="p">()</span>
    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">eliminate_dead_code</span><span class="p">()</span>
    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">lint</span><span class="p">()</span>

    <span class="k">return</span> <span class="n">gm</span>


<span class="k">def</span><span class="w"> </span><span class="nf">lift</span><span class="p">(</span>
    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">graph_signature</span><span class="p">:</span> <span class="n">Any</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">ExportGraphSignature</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Given an unlifted fx.GraphModule, lift all parameters, buffers into placeholders.</span>
<span class="sd">    Arguments:</span>
<span class="sd">        gm (torch.fx.GraphModule): Unlifted GraphModule which contains parameters and buffers as get_attr nodes.</span>
<span class="sd">        graph_signature (torch.export.ExportGraphSignature): Instance of ExportGraphSignature class created for the output ExportedProgram.</span>
<span class="sd">        After lifting, this graph_signature will be modified with the parameters and buffers added appropriately.</span>
<span class="sd">    Returns:</span>
<span class="sd">        A lifted fx.GraphModule, modified graph_signature and a new state_dict</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># Get the state_dict of graph_module. This is different from exported_program.state_dict</span>
    <span class="c1"># exp_program.state_dict contains parameters and buffers whereas a graph_module&#39;s state_dict</span>
    <span class="c1"># has all parameters registered as torch.tensors.</span>
    <span class="n">state_dict</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
    <span class="n">constants</span> <span class="o">=</span> <span class="p">{}</span>

    <span class="n">fake_mode</span> <span class="o">=</span> <span class="n">detect_fake_mode</span><span class="p">(</span>
        <span class="nb">tuple</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span><span class="p">)</span>
    <span class="p">)</span>
    <span class="k">assert</span> <span class="n">fake_mode</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>

    <span class="c1"># This map stores the names of outputs (old to new)</span>
    <span class="c1"># This is necessary to track because the output names can be changed when</span>
    <span class="c1"># we convert graph constants to placeholder inputs below.</span>
    <span class="n">output_names</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">for</span> <span class="n">output_spec</span> <span class="ow">in</span> <span class="n">graph_signature</span><span class="o">.</span><span class="n">output_specs</span><span class="p">:</span>
        <span class="n">output_names</span><span class="p">[</span><span class="n">output_spec</span><span class="o">.</span><span class="n">arg</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">output_spec</span><span class="o">.</span><span class="n">arg</span><span class="o">.</span><span class="n">name</span>

    <span class="c1"># Locate the user input to insert new placeholders before them</span>
    <span class="n">first_user_input</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span> <span class="ow">and</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">graph_signature</span><span class="o">.</span><span class="n">user_inputs</span><span class="p">:</span>
            <span class="n">first_user_input</span> <span class="o">=</span> <span class="n">node</span>
            <span class="k">break</span>

    <span class="c1"># At first the user_inputs are only present in the graph_signature.input_specs and hence non_user_input_idx=0</span>
    <span class="c1"># The input_specs should be of the form [params, buffers, constant_tensors, custom_obj, user_inputs]</span>
    <span class="n">non_user_input_idx</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;get_attr&quot;</span><span class="p">:</span>
            <span class="n">lift_val</span> <span class="o">=</span> <span class="kc">None</span>
            <span class="n">input_kind</span> <span class="o">=</span> <span class="kc">None</span>

            <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="p">:</span>
                <span class="n">constants</span><span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">]</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
                <span class="n">input_kind</span> <span class="o">=</span> <span class="n">InputKind</span><span class="o">.</span><span class="n">CUSTOM_OBJ</span>
                <span class="n">lift_val</span> <span class="o">=</span> <span class="n">constants</span><span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">]</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">lift_val</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">]</span>

                <span class="n">input_kind</span> <span class="o">=</span> <span class="n">InputKind</span><span class="o">.</span><span class="n">CONSTANT_TENSOR</span>

                <span class="c1"># state_dict has these parameters/buffers as torch.Tensors. We override them as torch.nn.Parameter/torch.Tensors respectively.</span>
                <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
                    <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span> <span class="o">==</span> <span class="n">name</span><span class="p">:</span>
                        <span class="n">input_kind</span> <span class="o">=</span> <span class="n">InputKind</span><span class="o">.</span><span class="n">PARAMETER</span>
                        <span class="n">state_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">state_dict</span><span class="p">[</span><span class="n">name</span><span class="p">])</span>
                        <span class="k">break</span>
                <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">named_buffers</span><span class="p">():</span>
                    <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span> <span class="o">==</span> <span class="n">name</span><span class="p">:</span>
                        <span class="n">input_kind</span> <span class="o">=</span> <span class="n">InputKind</span><span class="o">.</span><span class="n">BUFFER</span>
                        <span class="k">break</span>

            <span class="k">assert</span> <span class="n">lift_val</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">input_kind</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>

            <span class="c1"># Replace get_attr nodes with placeholder nodes and copy metadata.</span>
            <span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_before</span><span class="p">(</span><span class="n">first_user_input</span><span class="p">):</span>
                <span class="c1"># Ensure name doesn&#39;t contain period as it is used for submodules</span>
                <span class="n">const_placeholder_name</span> <span class="o">=</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="s2">&quot;_&quot;</span><span class="p">)</span>
                <span class="n">const_placeholder_node</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">const_placeholder_name</span><span class="p">)</span>
                <span class="c1"># Copy the node meta into this new placeholder node</span>
                <span class="n">const_placeholder_node</span><span class="o">.</span><span class="n">meta</span> <span class="o">=</span> <span class="n">node</span><span class="o">.</span><span class="n">meta</span>

                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">lift_val</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
                    <span class="n">const_placeholder_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span>
                        <span class="n">FakeTensor</span><span class="p">,</span>
                        <span class="n">torch</span><span class="o">.</span><span class="n">empty_strided</span><span class="p">(</span>
                            <span class="nb">tuple</span><span class="p">(</span><span class="n">lift_val</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
                            <span class="nb">tuple</span><span class="p">([</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">lift_val</span><span class="o">.</span><span class="n">shape</span><span class="p">)),</span>
                        <span class="p">),</span>
                    <span class="p">)</span>

                <span class="n">node</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span><span class="n">const_placeholder_node</span><span class="p">)</span>
                <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">erase_node</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>

                <span class="c1"># Verify if the const_placeholder being added is one of the output nodes</span>
                <span class="c1"># This happens if there is just a single static arange op in the graph</span>
                <span class="c1"># https://github.com/pytorch/TensorRT/issues/3189</span>
                <span class="k">if</span> <span class="n">const_placeholder_name</span> <span class="ow">in</span> <span class="n">output_names</span><span class="p">:</span>
                    <span class="n">output_names</span><span class="p">[</span><span class="n">const_placeholder_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">const_placeholder_node</span><span class="o">.</span><span class="n">name</span>

                <span class="c1"># Add these parameters/buffers/constants to the existing graph signature</span>
                <span class="c1"># before user inputs. These specs are looked up in the state_dict during ExportedProgram creation.</span>
                <span class="n">input_spec_arg</span> <span class="o">=</span> <span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">const_placeholder_node</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
                <span class="k">if</span> <span class="n">input_kind</span> <span class="o">==</span> <span class="n">InputKind</span><span class="o">.</span><span class="n">CUSTOM_OBJ</span><span class="p">:</span>
                    <span class="n">input_spec_arg</span> <span class="o">=</span> <span class="n">CustomObjArgument</span><span class="p">(</span>
                        <span class="n">name</span><span class="o">=</span><span class="n">const_placeholder_node</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">class_fqn</span><span class="o">=</span><span class="s2">&quot;&quot;</span>
                    <span class="p">)</span>
                <span class="n">graph_signature</span><span class="o">.</span><span class="n">input_specs</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span>
                    <span class="n">non_user_input_idx</span><span class="p">,</span>
                    <span class="n">InputSpec</span><span class="p">(</span>
                        <span class="n">kind</span><span class="o">=</span><span class="n">input_kind</span><span class="p">,</span>
                        <span class="n">arg</span><span class="o">=</span><span class="n">input_spec_arg</span><span class="p">,</span>
                        <span class="n">target</span><span class="o">=</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">,</span>
                    <span class="p">),</span>
                <span class="p">)</span>
                <span class="n">non_user_input_idx</span> <span class="o">+=</span> <span class="mi">1</span>

    <span class="c1"># Update output_specs with modified names. This only gets updated if the graph getattr nodes (weights)</span>
    <span class="c1"># are also the outputs of the graph</span>
    <span class="k">for</span> <span class="n">output_spec</span> <span class="ow">in</span> <span class="n">graph_signature</span><span class="o">.</span><span class="n">output_specs</span><span class="p">:</span>
        <span class="n">output_spec</span><span class="o">.</span><span class="n">arg</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">output_names</span><span class="p">[</span><span class="n">output_spec</span><span class="o">.</span><span class="n">arg</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>

    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">eliminate_dead_code</span><span class="p">()</span>
    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">lint</span><span class="p">()</span>

    <span class="k">return</span> <span class="n">gm</span><span class="p">,</span> <span class="n">graph_signature</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">constants</span>


<span class="k">def</span><span class="w"> </span><span class="nf">get_duplicate_nodes</span><span class="p">(</span>
    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">submodule</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">],</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    We check if there are duplicate nodes when we copy submodule graph into gm.</span>
<span class="sd">    Handle the case where the subgraph input placeholders are same as</span>
<span class="sd">    gm placeholders. This happens when the first submodule in the graph is</span>
<span class="sd">    a pytorch submodule</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">submodule_placeholder_inputs</span> <span class="o">=</span> <span class="p">[</span>
        <span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">submodule</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span>
    <span class="p">]</span>
    <span class="n">submodule_input_node_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">submodule_placeholder_inputs</span><span class="p">]</span>
    <span class="n">gm_node_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">]</span>
    <span class="n">submodule_duplicate_inputs</span> <span class="o">=</span> <span class="p">[</span>
        <span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">submodule_placeholder_inputs</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">gm_node_names</span>
    <span class="p">]</span>
    <span class="n">gm_duplicate_inputs</span> <span class="o">=</span> <span class="p">[</span>
        <span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">submodule_input_node_names</span>
    <span class="p">]</span>
    <span class="k">return</span> <span class="n">submodule_duplicate_inputs</span><span class="p">,</span> <span class="n">gm_duplicate_inputs</span>


<span class="k">def</span><span class="w"> </span><span class="nf">inline_torch_modules</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Inline a submodule within the parent graph (gm). All `call_module` nodes</span>
<span class="sd">    should be replaced by their nodes in the submodule.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># Clean the graph</span>
    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">eliminate_dead_code</span><span class="p">()</span>
    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">lint</span><span class="p">()</span>

    <span class="k">for</span> <span class="n">gm_node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">gm_node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_module&quot;</span> <span class="ow">and</span> <span class="s2">&quot;_run_on_gpu&quot;</span> <span class="ow">in</span> <span class="n">gm_node</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
            <span class="n">submodule</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">gm_node</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
            <span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_before</span><span class="p">(</span><span class="n">gm_node</span><span class="p">):</span>
                <span class="c1"># Get inputs of submodule node which are most likely outputs of a previous TRT node</span>
                <span class="c1"># or a placeholder of the main graph</span>
                <span class="n">submodule_inputs</span> <span class="o">=</span> <span class="n">gm_node</span><span class="o">.</span><span class="n">args</span>

                <span class="n">submodule_duplicate_inputs</span><span class="p">,</span> <span class="n">gm_duplicate_inputs</span> <span class="o">=</span> <span class="n">get_duplicate_nodes</span><span class="p">(</span>
                    <span class="n">gm</span><span class="p">,</span> <span class="n">submodule</span>
                <span class="p">)</span>
                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">submodule_duplicate_inputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">gm_duplicate_inputs</span><span class="p">)</span>
                <span class="c1"># Avoid creating new copies of duplicate inputs by creating a mapping</span>
                <span class="n">val_map</span> <span class="o">=</span> <span class="p">{}</span>
                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">submodule_duplicate_inputs</span><span class="p">)):</span>
                    <span class="n">val_map</span><span class="p">[</span><span class="n">submodule_duplicate_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="n">gm_duplicate_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>

                <span class="c1"># Copy all nodes in the submodule into gm and</span>
                <span class="c1"># store the output node of this submodule which is now present in gm</span>
                <span class="n">submodule_output</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">graph_copy</span><span class="p">(</span><span class="n">submodule</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> <span class="n">val_map</span><span class="p">)</span>

                <span class="c1"># Get their references (since we copied) in the parent graph (gm)</span>
                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">submodule_duplicate_inputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                    <span class="n">submodule_placeholder_input_names</span> <span class="o">=</span> <span class="p">[</span>
                        <span class="n">node</span><span class="o">.</span><span class="n">name</span>
                        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">submodule</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span>
                        <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span>
                    <span class="p">]</span>
                    <span class="n">gm_added_placeholder_inputs</span> <span class="o">=</span> <span class="p">[</span>
                        <span class="n">node</span>
                        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span>
                        <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">submodule_placeholder_input_names</span>
                    <span class="p">]</span>

                    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">submodule_inputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">gm_added_placeholder_inputs</span><span class="p">)</span>

                    <span class="c1"># Replace the added placeholder inputs with original inputs to this submodule node</span>
                    <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">gm_added_placeholder_inputs</span><span class="p">)):</span>
                        <span class="n">gm_added_placeholder_inputs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span>
                            <span class="n">submodule_inputs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
                        <span class="p">)</span>

                    <span class="c1"># Erase the placeholder input nodes in the gm</span>
                    <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">gm_added_placeholder_inputs</span><span class="p">)):</span>
                        <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">erase_node</span><span class="p">(</span><span class="n">gm_added_placeholder_inputs</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>

                <span class="c1"># Replace the pytorch submodule node (call_module) with the inlined subgraph output</span>
                <span class="n">gm_node</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span><span class="n">submodule_output</span><span class="p">)</span>

                <span class="c1"># copy the attributes of the submodule into gm (graph_copy doesn&#39;t do this)</span>
                <span class="n">copy_submodule_attributes</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">submodule</span><span class="p">,</span> <span class="n">gm_node</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>

            <span class="c1"># Erase the pytorch submodule (call_module) node</span>
            <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">erase_node</span><span class="p">(</span><span class="n">gm_node</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">gm</span>


<span class="k">def</span><span class="w"> </span><span class="nf">copy_submodule_attributes</span><span class="p">(</span>
    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">submodule</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">submodule_name</span><span class="p">:</span> <span class="nb">str</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    The submodule parameters are available in the parent gm&#39;s state_dict, but they have</span>
<span class="sd">    the submodule name as a prefix in their keys. For eg: gm.state_dict() would have</span>
<span class="sd">    _run_on_gpu_0.conv.weight etc. Since we graph copied the submodule into gm, we should</span>
<span class="sd">    also copy it&#39;s parameters and buffers into gm without the submodule namespace as prefix.</span>
<span class="sd">    _assign_attr does exactly that. It creates a module for eg: conv, adds an attribute weight</span>
<span class="sd">    to it and adds this conv module as an attribute to parent gm.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch.export.unflatten</span><span class="w"> </span><span class="kn">import</span> <span class="n">_assign_attr</span><span class="p">,</span> <span class="n">_AttrKind</span>

    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">submodule</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
        <span class="n">_assign_attr</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">gm</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">_AttrKind</span><span class="o">.</span><span class="n">PARAMETER</span><span class="p">)</span>

    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">submodule</span><span class="o">.</span><span class="n">named_buffers</span><span class="p">():</span>
        <span class="n">_assign_attr</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">gm</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">_AttrKind</span><span class="o">.</span><span class="n">BUFFER</span><span class="p">)</span>


<span class="k">def</span><span class="w"> </span><span class="nf">create_trt_exp_program</span><span class="p">(</span>
    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ExportedProgram</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a new Exported Program. This function takes an torch.fx.GraphModule which has TRT engines</span>
<span class="sd">    and constructs an Exported Program object with the new IO node names and state_dict</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">input_nodes</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span><span class="p">]</span>
    <span class="n">output_nodes</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;output&quot;</span><span class="p">]</span>
    <span class="k">assert</span> <span class="n">output_nodes</span>
    <span class="n">output_nodes</span> <span class="o">=</span> <span class="n">output_nodes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

    <span class="n">input_specs</span> <span class="o">=</span> <span class="p">[</span>
        <span class="n">InputSpec</span><span class="p">(</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">,</span> <span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="p">),</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">input_nodes</span>
    <span class="p">]</span>
    <span class="n">output_specs</span> <span class="o">=</span> <span class="p">[</span>
        <span class="n">OutputSpec</span><span class="p">(</span><span class="n">OutputKind</span><span class="o">.</span><span class="n">USER_OUTPUT</span><span class="p">,</span> <span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="p">),</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">output_nodes</span>
    <span class="p">]</span>

    <span class="n">trt_graph_signature</span> <span class="o">=</span> <span class="n">ExportGraphSignature</span><span class="p">(</span>
        <span class="n">input_specs</span><span class="o">=</span><span class="n">input_specs</span><span class="p">,</span> <span class="n">output_specs</span><span class="o">=</span><span class="n">output_specs</span>
    <span class="p">)</span>

    <span class="n">module_call_graph</span> <span class="o">=</span> <span class="p">[</span>
        <span class="n">ModuleCallEntry</span><span class="p">(</span>
            <span class="s2">&quot;&quot;</span><span class="p">,</span>
            <span class="n">ModuleCallSignature</span><span class="p">(</span>
                <span class="n">inputs</span><span class="o">=</span><span class="p">[],</span>
                <span class="n">outputs</span><span class="o">=</span><span class="p">[],</span>
                <span class="n">in_spec</span><span class="o">=</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">_codegen</span><span class="o">.</span><span class="n">pytree_info</span><span class="o">.</span><span class="n">in_spec</span><span class="p">,</span>
                <span class="n">out_spec</span><span class="o">=</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">_codegen</span><span class="o">.</span><span class="n">pytree_info</span><span class="o">.</span><span class="n">out_spec</span><span class="p">,</span>
            <span class="p">),</span>
        <span class="p">)</span>
    <span class="p">]</span>

    <span class="c1"># Lift parameters/buffers/constants in the graph</span>
    <span class="c1"># torch.export serialization expects them to be lifted</span>
    <span class="n">gm</span><span class="p">,</span> <span class="n">trt_graph_signature</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">constants</span> <span class="o">=</span> <span class="n">lift</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">trt_graph_signature</span><span class="p">)</span>

    <span class="n">trt_exp_program</span> <span class="o">=</span> <span class="n">ExportedProgram</span><span class="p">(</span>
        <span class="n">root</span><span class="o">=</span><span class="n">gm</span><span class="p">,</span>
        <span class="n">graph</span><span class="o">=</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span>
        <span class="n">graph_signature</span><span class="o">=</span><span class="n">trt_graph_signature</span><span class="p">,</span>
        <span class="n">state_dict</span><span class="o">=</span><span class="n">state_dict</span><span class="p">,</span>
        <span class="n">range_constraints</span><span class="o">=</span><span class="p">{},</span>
        <span class="n">module_call_graph</span><span class="o">=</span><span class="n">module_call_graph</span><span class="p">,</span>
        <span class="n">constants</span><span class="o">=</span><span class="n">constants</span><span class="p">,</span>
    <span class="p">)</span>

    <span class="k">return</span> <span class="n">trt_exp_program</span>


<span class="k">def</span><span class="w"> </span><span class="nf">inline_trt_modules</span><span class="p">(</span>
    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">cross_compile_module</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Replace TRT submodules with trt engine nodes.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
        <span class="k">if</span> <span class="s2">&quot;_run_on_acc&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">name</span><span class="p">:</span>
            <span class="k">continue</span>
        <span class="c1"># Get the TRT submodule</span>
        <span class="n">trt_module</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>

        <span class="c1"># Ensure the trt module node in the main graph (gm) has inputs</span>
        <span class="n">trt_module_node</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="n">name</span><span class="p">]</span>
        <span class="k">assert</span> <span class="n">trt_module_node</span>
        <span class="n">trt_module_node</span> <span class="o">=</span> <span class="n">trt_module_node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="k">assert</span> <span class="n">trt_module_node</span><span class="o">.</span><span class="n">args</span>

        <span class="k">if</span> <span class="s2">&quot;val&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">trt_module_node</span><span class="o">.</span><span class="n">meta</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;trt_module_node: </span><span class="si">{</span><span class="n">trt_module_node</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2"> does not have the metadata which should be set during dynamo compile_module step.&quot;</span>
            <span class="p">)</span>
        <span class="n">num_outputs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">trt_module_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">])</span>
        <span class="c1"># Insert a call_function node to perform inference on TRT engine</span>
        <span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_before</span><span class="p">(</span><span class="n">trt_module_node</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">cross_compile_module</span><span class="p">:</span>
                <span class="n">engine_info</span> <span class="o">=</span> <span class="n">trt_module</span><span class="o">.</span><span class="n">_pack_engine_info</span><span class="p">()</span>
                <span class="n">engine_bytes</span> <span class="o">=</span> <span class="n">engine_info</span><span class="p">[</span><span class="n">ENGINE_IDX</span><span class="p">]</span>
                <span class="n">engine_info</span><span class="p">[</span><span class="n">ENGINE_IDX</span><span class="p">]</span> <span class="o">=</span> <span class="n">base64</span><span class="o">.</span><span class="n">b64encode</span><span class="p">(</span><span class="n">engine_bytes</span><span class="p">)</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
                <span class="c1"># insert the no_placeholder node in the graph which should be replaced to the actual execute_engine node while load in the windows</span>
                <span class="n">trt_node</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span>
                    <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">no_op_placeholder_for_execute_engine</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
                    <span class="p">(</span><span class="n">trt_module_node</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="o">*</span><span class="n">engine_info</span><span class="p">),</span>
                <span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="c1"># for the normal workflow: use the execute_engine node</span>
                <span class="n">engine_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">_engine&quot;</span>
                <span class="nb">setattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">engine_name</span><span class="p">,</span> <span class="n">trt_module</span><span class="o">.</span><span class="n">engine</span><span class="p">)</span>
                <span class="n">engine_node</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">get_attr</span><span class="p">(</span><span class="n">engine_name</span><span class="p">)</span>

                <span class="n">trt_node</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span>
                    <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">execute_engine</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
                    <span class="p">(</span><span class="n">trt_module_node</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="n">engine_node</span><span class="p">),</span>
                <span class="p">)</span>
                <span class="c1"># meta[&quot;val&quot;] should be a lighter version of a tensor. For eg: it should be a FakeTensor (with output shape and dtype properties)</span>
                <span class="c1"># Lighter version of a custom_obj is not defined clearly. meta[&quot;val&quot;] does not have any type expectations but</span>
                <span class="c1"># for custom object nodes, it should be CustomObjArgument</span>
                <span class="n">engine_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">CustomObjArgument</span><span class="p">(</span>
                    <span class="n">name</span><span class="o">=</span><span class="n">engine_node</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">class_fqn</span><span class="o">=</span><span class="s2">&quot;&quot;</span>
                <span class="p">)</span>
            <span class="c1"># set trt_node.meta with trt_module_node.meta</span>
            <span class="k">assert</span> <span class="n">num_outputs</span> <span class="o">&gt;</span> <span class="mi">0</span>
            <span class="n">trt_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">trt_module_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span>

        <span class="k">if</span> <span class="n">num_outputs</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="c1"># Insert getitem nodes as outputs (for export serialization to work)</span>
            <span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_after</span><span class="p">(</span><span class="n">trt_node</span><span class="p">):</span>
                <span class="n">getitem_output</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span><span class="n">operator</span><span class="o">.</span><span class="n">getitem</span><span class="p">,</span> <span class="p">(</span><span class="n">trt_node</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
                <span class="n">getitem_output</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">trt_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span>
            <span class="n">trt_module_node</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span><span class="n">getitem_output</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="c1"># Multiple outputs case:</span>
            <span class="c1"># Replace uses of submodule with the trt_node.</span>
            <span class="c1"># getitem nodes are already added inherently by the partitioner</span>
            <span class="n">trt_module_node</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span><span class="n">trt_node</span><span class="p">)</span>
            <span class="n">getitem_nodes</span> <span class="o">=</span> <span class="n">trt_node</span><span class="o">.</span><span class="n">users</span>
            <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">getitem_node</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">getitem_nodes</span><span class="p">):</span>
                <span class="n">getitem_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">trt_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">][</span><span class="n">idx</span><span class="p">]</span>

        <span class="c1"># Erase the TRT submodule (call_module) node.</span>
        <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">erase_node</span><span class="p">(</span><span class="n">trt_module_node</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">gm</span>


<span class="k">def</span><span class="w"> </span><span class="nf">replace_execute_engine_no_op_node</span><span class="p">(</span>
    <span class="n">exp_program</span><span class="p">:</span> <span class="n">ExportedProgram</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ExportedProgram</span><span class="p">:</span>
    <span class="n">gm</span> <span class="o">=</span> <span class="n">exp_program</span><span class="o">.</span><span class="n">graph_module</span>
    <span class="n">no_op_placeholder_nodes</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
        <span class="k">if</span> <span class="s2">&quot;no_op_placeholder_for_execute_engine&quot;</span> <span class="ow">in</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
            <span class="n">no_op_placeholder_nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>
    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">no_op_placeholder_nodes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">no_op_placeholder_node</span> <span class="ow">in</span> <span class="n">no_op_placeholder_nodes</span><span class="p">:</span>
        <span class="k">if</span> <span class="s2">&quot;val&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">no_op_placeholder_node</span><span class="o">.</span><span class="n">meta</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;metadata info is missing for the node: </span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
        <span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_before</span><span class="p">(</span><span class="n">no_op_placeholder_node</span><span class="p">):</span>
            <span class="n">packed_engine_info</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">no_op_placeholder_node</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
            <span class="n">engine_bytes</span> <span class="o">=</span> <span class="n">packed_engine_info</span><span class="p">[</span><span class="n">ENGINE_IDX</span><span class="p">]</span>
            <span class="n">engine_name</span> <span class="o">=</span> <span class="n">packed_engine_info</span><span class="p">[</span><span class="n">NAME_IDX</span><span class="p">]</span>

            <span class="n">packed_engine_info</span><span class="p">[</span><span class="n">ENGINE_IDX</span><span class="p">]</span> <span class="o">=</span> <span class="n">base64</span><span class="o">.</span><span class="n">b64decode</span><span class="p">(</span>
                <span class="n">engine_bytes</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
            <span class="p">)</span>
            <span class="n">trt_engine</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">Engine</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">packed_engine_info</span><span class="p">))</span>
            <span class="nb">setattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">engine_name</span><span class="p">,</span> <span class="n">trt_engine</span><span class="p">)</span>
            <span class="n">engine_node</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">get_attr</span><span class="p">(</span><span class="n">engine_name</span><span class="p">)</span>

            <span class="n">trt_node</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">execute_engine</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
                <span class="p">(</span><span class="n">no_op_placeholder_node</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">engine_node</span><span class="p">),</span>
            <span class="p">)</span>
            <span class="n">trt_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">no_op_placeholder_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span>
            <span class="n">engine_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">CustomObjArgument</span><span class="p">(</span>
                <span class="n">name</span><span class="o">=</span><span class="n">engine_node</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">class_fqn</span><span class="o">=</span><span class="s2">&quot;&quot;</span>
            <span class="p">)</span>

        <span class="n">no_op_placeholder_node</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span><span class="n">trt_node</span><span class="p">)</span>
        <span class="n">getitem_nodes</span> <span class="o">=</span> <span class="n">trt_node</span><span class="o">.</span><span class="n">users</span>
        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">getitem_node</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">getitem_nodes</span><span class="p">):</span>
            <span class="n">getitem_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">trt_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">][</span><span class="n">idx</span><span class="p">]</span>

        <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">erase_node</span><span class="p">(</span><span class="n">no_op_placeholder_node</span><span class="p">)</span>

    <span class="n">gm</span><span class="o">.</span><span class="n">delete_all_unused_submodules</span><span class="p">()</span>
    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">eliminate_dead_code</span><span class="p">()</span>
    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">lint</span><span class="p">()</span>
    <span class="n">gm</span><span class="o">.</span><span class="n">recompile</span><span class="p">()</span>

    <span class="k">return</span> <span class="n">exp_program</span>
</pre></div>

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